Analysis of Karamu Catchment Flow Regimes and Water Supply Security Under a Range of Water Allocation Scenarios

Transcription

1 Analysis of Karamu Catchment Flow Regimes and Water Supply Security Under a Range of Water Allocation Scenarios HBRC (2004) Mar 2008 Page EMT 08/04 HBRC Plan Number 4013

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3 Environmental Management Group Technical Report Environmental Science Section Analysis of Karamu Catchment Flow Regimes and Water Supply Security Under a Range of Water Allocation Scenarios Prepared by: Rob Waldron Hydrology Data Analyst Reviewed by: Mike Harkness Senior Hydrologist, MWH New Zealand Limited Approved by: Graham Sevicke-Jones Manager, Environmental Science Reviewed: Mar 2008 EMT 08/04 HBRC Plan Number 4013 Copyright: Hawke s Bay Regional Council

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5 EXECUTIVE SUMMARY The Hawke s Bay Regional Council manages the region s water resources in accordance with the Regional Resource Management Plan and the Resource Management Act Surface water is managed using allocatable volumes and minimum flows for specific rivers/streams in the region. The HBRC is currently processing 68 consents for the taking of surface water from the Karamu Stream and its tributaries. The renewal process is also considering a number of groundwater takes which are situated within 400m of surface water, and are considered of medium to high risk for depleting the surface water resources of the Karamu catchment. This study was undertaken to identify the effects on flow variability on the Karamu Stream, by changing the allocatable volume for the site, Karamu Stream at Floodgates. A naturalised flow record was produced for the site for the period 1989 to Calculating an allocatable volume for the Karamu Stream based on the current minimum flow (set in 1990) suggests that the resource is over allocated. Re-calculating the minimum flow using the naturalised flow record and the same methods as in 1998, produced a lower possible minimum flow of 913l/s. However this value does not take into account environmental and habitat values. If a lower minimum flow was established, more water would be available for allocation. The naturalised flow record and current minimum flow of 1100l/s was used to model the effects of different abstraction demands, based on current and possible future allocatable volumes. Using LowFAT, a computer program developed by NIWA Science, two types of extraction demand were modelled: abstraction demands based on a maximum constant rate of take abstraction demands based on a maximum instantaneous rate of take Results showed that increasing abstraction demand reduced flow variability in the low flow range, and increased the number of days abstraction that would be likely under restriction. Higher total instantaneous rates of abstraction have the greatest impact when flows are near to the minimum flow threshold. As they increase they become more difficult to manage, in terms of implementing restrictions and preventing streamflow from breaching the minimum flow. Page i

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7 TABLE OF CONTENTS 1.0 INTRODUCTION Background Scope Study Objectives Karamu Stream Characteristics Water Allocation Minimum Flows Method Summary Data Limitations NATURALISED KARAMU FLOW RECORD Developing the Flow Correlation Validation of the Flow Correlation Producing the Naturalised Flow Record NATURALISED KARAMU FLOW STATISTICS LOWFAT MODELLING LowFAT Abstraction Scenarios LowFAT Project LowFAT Project Scenario Results - LowFAT Project Flow Distributions Restriction Days Scenario Results - LowFAT Project Flow Comparisons for 10 Worst Low Flow Periods Flow Duration RESULTS Karamu Flow Statistics LowFAT Project 1 Scenarios LowFAT Project 2 Scenarios CONCLUSIONS Karamu Flow Statistics LowFAT Project 1 Scenarios LowFAT Project 2 Scenarios FURTHER INVESTIGATIONS ACKNOWLEDGEMENTS REFERENCES APPENDIX APPENDIX Page ii

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9 1.0 INTRODUCTION 1.1 Background The Hawke s Bay Regional Council (HBRC) manages the region s water resources in accordance with the Regional Resource Management Plan (RRMP) 2006, and the Resource Management Act (RMA) Allocatable volumes 1 and minimum flows 2 have been established on specific rivers/streams (see Table 1), in order to support social, cultural, economic and environmental needs of the region. The HBRC is currently processing 68 consents for the taking of surface water from the Karamu Stream and its tributaries. These consents expired in May The process to renew these consents must consider what is an appropriate allocatable volume of water within the Karamu catchment. In July 2007 at the HBRC Environmental Management Committee meeting, the paper by Lew (2007) identified that the RRMP allocation policy for the Karamu Catchment may no longer be defensible and valid for this renewal process. This policy was first adopted for the Ngaruroro River in the mid 1990s to define its allocatable volume, but it does not account for the different flow regimes or current volumes allocated within the Karamu Catchment. In addition to direct surface water takes, the renewal process is also considering a number of groundwater takes which are situated within 400m of surface water. These are considered as medium to high risk (Larking, 2004) for depleting the surface water resources of the Karamu catchment. The Hawke s Bay region is divided into consent renewal areas and stream management zones to better manage water resources and increase efficiency in the consent process. The Stream Management Zones (shown in Figure 1) are based on the catchment areas for each minimum flow site (control site) in the Karamu system. 1 Allocatable volume (RRMP definition): The volume of water flow available for out-of-stream use e.g. irrigation. It is the volume of the total river flow available over a set period (e.g. the average daily flow or average seven day flow) that may be abstracted from a river or stream without causing the minimum flow to occur so often as to cause a continuing change in the nature of the aquatic ecosystem. 2 Minimum Flow (RRMP definition):a critical flow set to ensure sufficient water is left in a river to maintain the life-supporting capacity of aquatic ecosystems and/or other identified values, during low flow conditions. Page 1

10 Figure 1. Karamu Stream Management Zone Map Page 2

11 1.2 Scope This study was undertaken to identify the effects on flow variability on the Karamu Stream, by changing the allocatable volume for the site, Karamu Stream at Floodgates. To evaluate these effects on flow variability a naturalised flow 3 record for the site was first needed. The HBRC has been unable to produce a rated flow record for the site, due to a continuously changing bed profile caused by the abundance of weed growth. This report details how a naturalised flow record was produced for the site and documents the analysis undertaken using this flow record. The findings of this report will be used to help determine at what limit the allocatable volume for the Karamu Stream should be set. 1.3 Study Objectives To evaluate different allocation levels at the control site, Karamu Stream at Floodgates. This is the primary site for analysis. Data from this study will be used to review allocation levels for all control sites specified in the RRMP, within the Karamu catchment. 1.4 Karamu Stream Characteristics The Karamu Stream has a very low gradient and a steady groundwater derived flow and experiences heavy aquatic weed growth. During the summer months, high temperatures (an average of 19ºC from gauging records) and an overall lack of shade increase the growth of weed, significantly raising water levels, and so making it difficult to produce an accurate rated flow. Karamu Stream at Floodgates (Figure 2) is the downstream site for the whole of the Karamu system, with the exception of the Raupare Stream. The confluence of the Karamu and the Raupare streams mark the upper limit of the Clive River. 3 Naturalised flow: Flow that would occur without the effect of abstraction Page 3

12 Figure 2. Site Location Map Page 4

13 1.5 Water Allocation The RRMP defines the allocatable volume for the Karamu catchment as being the difference between the summer 4 7-day Q95 flow and the minimum flow 5. In the Karamu catchment the main control site, Karamu Stream at Floodgates, has an allocatable volume set at 18,023m 3 /week (see Table 1). However the current total allocated volume from surface water takes and including medium to high risk groundwater takes, is 22,445m 3 /week, which exceeds the current allocatable volume limit. The other control sites in the catchment, either have no available water to allocate (represented by a zero) or an allocatable volume has not yet been determined for the site (represented by a dash). Table 1. Minimum Flow and Allocatable Volumes for the Karamu Stream and Tributaries (Lew, 2007) River name Minimum Flow Site Name Minimum Flow (l/s) Allocatable Volume (m3/week) Total Number of Consents and (Allocated Volume from Surface Water Consents m3/week) Total Number of Consents and (Allocated Volume from Surface and Medium to High Risk Groundwater Consents m3/week) Awanui Stream At The Flume (2373) 4 (3727) Ongaru Drain At Wenley Road (21225) 3 (21225) Karewarewa Stream At Turamoe Road 75-5 (10650) 10 (71496) Poukawa Inflow Site No (8870) 1 (8870) Poukawa Inflow Site No.1a (u/s dam) Poukawa Inflow Site No.1 (d/s dam) Poukawa Stream At Douglas Road (8637) 6 (8637) Awanui Stream At Pakipaki Culvert (5850) 2 (18910) Irongate Stream At Clarks Weir (360) 4 (5755) Te Waikaha Stream At Mutiny Road 25-1 (4772) 1 (4772) Louisa Stream At Te Aute Road (19101) 5 (19101) Mangateretere Stream At Napier Road ( ) Karamu Stream At Floodgates (9090) 19 (22445) Unassigned (34350) 11 (34350) 1.6 Minimum Flows Minimum flow 6 is a critical flow set to ensure sufficient water is left in a river to maintain the lifesupporting capacity of aquatic ecosystems and/or other identified values, during low flow conditions except when flow naturally falls below. The minimum flow is the flow at which abstractions cease, except for primary domestic supplies, stock water and fire fighting. The minimum flow for Karamu Stream at Floodgates is set at 1100l/s. This and the minimum flows on other streams in the Karamu catchment were set in 1990, based on work by Porter and Cairns (1990), involving fish habitat assessment methods. These minimum flows were incorporated into the Regional Water Resources Plan (HBRC 1995). Although the assessments were ecologically based on local knowledge, there was no explanation of the criteria for choosing a minimum flow. In 1998, minimum flows across the region were reviewed as part of the Sustainable Low Flow Project (SLFP) (Woods, 2002). In this project, the approach to defining minimum flows was based on using Instream Flow Incremental Methodology (IFIM) to identify flows that provide habitat for a range of fish and aquatic invertebrate species. It was found that in smaller streams, summer flow conditions were unable to provide the level of habitat suggested by the results of habitat modeling. Acceptable habitat levels were therefore assessed against the 4 Summer refers to the months, November to April inclusive 5 Section RRMP - Criteria for setting minimum flows 6 Policy 74 (a) RRMP - Resource Allocation Page 5

14 Mean Annual Low Flow (MALF), an indicator of flows that may be expected to occur each year in a typical Hawke s Bay summer. It was proposed that where no habitat investigations had been carried out, a figure of 80% of the MALF should be used as the minimum flow. This was to reflect the degree of variation in summer flow conditions that regularly occur in dry East Coast catchments. The MALF was estimated for the Karamu Stream and tributaries by correlation with values from the recorder sites at Awanui Stream at Flume and Irongate Stream at Clarkes Weir. From these MALF estimates a lower minimum flow of 933l/s was proposed for the Karamu Stream. Following the completion of the SLFP the minimum flow for the Karamu Stream was kept at 1100l/s. During the summer low flow period, the HBRC undertakes weekly low flow gaugings at all low flow monitoring sites (see Figure 2). This provides the current status of flow in these streams. According to their status, irrigation restrictions will be placed on streams to help prevent them breaching their minimum flow (Table 1). 1.7 Method Summary The study is made up of three parts: 1 Producing a naturalised flow record for the site; Karamu Stream at Floodgates 2 Modelling the effects on the naturalised flow record by changing the abstraction demand (allocation level). Two types of abstraction demand are modelled: Abstraction demand based on a maximum constant rate of take applied to the months of the irrigation season 7 Abstraction demand based on a maximum instantaneous rate of take applied to the entire flow record 3 Discussing the effects on flow variability for the different levels of allocation Figure 3 details the study process used to produce the naturalised flow record and model allocation levels. 7 November to April inclusive Page 6

15 Figure 3. Method Summary Flowchart Page 7

16 1.8 Data Limitations Tables 2 and 3 summarise the respective gauged and rated flow data used in this study, Table 2. Gauged Flow Record Data Site Easting Northing Catchment Area (km 2 ) Record Length Gauged Flow Record Number of Gaugings Awanui Stream at Flume Irongate Stream at Clarkes Weir Karamu Stream at Floodgates Min Flow (l/s) Max Table 3. Rated Flow Record Data Site Full Record Effective Record Rated Flow Record % Missing Record Flow (l/s) Full Effective Min Max Mean Std Dev Awanui Stream at Flume Irongate Stream at Clarkes Weir Karamu Stream at Floodgates NA NA NA NA NA NA NA NA NA CV Limitations of the data include: Length of record: The shortest rated flow record (Awanui) determined the length of the naturalised Karamu flow record that could be derived (Section 2). Missing Record: This determined the gaps in the naturalised Karamu flow record. The Irongate Stream had the greater percentage of missing record. Time lags relating to flow: Reviewing the historical stage and flow records for the Awanui, Irongate and Karamu Streams, identified time lags of up to 5 days between the Awanui and Irongate sites and the downstream Karamu at Floodgates site. The lower the flows in the Karamu system, the greater the time lag. This is most noticeable when flow is below 100l/s. Coefficient of variation (CV): This can be used as a way of indexing flow variability. In Hawke s Bay most rivers are likely to have a CV between 1 and 2 (Lew et al, 1997). The statistics in Table 3 show the Awanui with a CV of 1.96 and the Irongate with a CV of 0.77 (outside the expected range). The Awanui s higher CV and therefore higher variability is most likely due to the artificial releases of water that take place in its tributaries (defined below). The Irongate is groundwater fed, with a small catchment (low runoff) and no major artificial influences, producing more stable flows and lower variability. Artificial releases: The Awanui Stream has two main tributaries, the Poukawa and Karewarewa Streams. Streamflow in the Awanui, is affected by the artificial release of water from Lake Poukawa into the Poukawa Stream, resulting in elevated flows in the Awanui. In the Karewarewa Stream, a consented activity which ceased in 1997, was used to supplement flows in the Karewarewa, also increasing flow in the Awanui. These effects on flow from both tributaries can be seen in the Awanui flow record. Mangateretere Stream: Streamflow at the Karamu at Floodgates site, can be influenced by inputs to the Karamu Stream from other areas of the catchment such as the Mangateretere Stream. Pumping from groundwater takes effects the Mangateretere Stream by lowering the flow and can influence flows in the Karamu, that will not be reflected in the Awanui and Irongate flow records. Page 8

17 2.0 NATURALISED KARAMU FLOW RECORD To produce a naturalised flow record for Karamu Stream at Floodgates site, a correlation using gauged and rated flow records for the following three sites was developed (see Figure 2 for site locations): Awanui Stream at Flume Irongate Stream at Clarkes Weir Karamu Stream at Floodgates The Awanui and Irongate sites were chosen for the correlation as they are two of the three main tributaries making up the flow in the Karamu mainstem. They have the largest amount of data for use in deriving a flow correlation. The length of the available rated flow records correlated, determined the length of the Karamu at Floodgates flow record that could be produced by correlation. The third main tributary to the Karamu Stream is the Mangateretere Stream. The flow record for the site, Mangateretere Stream at Napier Road, was not used in the correlation as this site has only a short flow record ( ). Both the Awanui and Irongate records are much longer. The Awanui record being the shorter of the two ( ) and thus defining the length of the final Karamu at Floodgates record. The Mangateretere Stream flow is also influenced by pumping from groundwater takes. The correlation (detailed in Section 2.1) used the flow gaugings undertaken at the Karamu Stream at Floodgates site, correlated with the sum of the Awanui and Irongate gauged flows. The Awanui and Irongate rated flow records were naturalised prior to the correlation process. The hydrological data used to produce the naturalised Karamu Stream flow record is stored in the HBRC s Hilltop Database. Page 9

18 2.1 Developing the Flow Correlation To create a correlation, gauged flows recorded at all three sites on the same day are needed. The shortest rated flow record belongs to the Awanui, starting in June Gauged and rated flows from this date onwards were used to construct the correlation. For a data point to be used in the correlation, there had to be a flow value for both the Awanui and Irongate to go with the gauged flow on the Karamu. The Karamu flow could then be plotted against the sum of the Awanui and Irongate flows. Both the Awanui and Irongate sites are weir sites with reliable continuous rated flow records. In order to increase the number of data points in the correlation, where there was a gauged flow for the Karamu, but not for either the Awanui or Irongate, these missing gauged flows were substituted with a rated flow value. Rated flow values for both the Awanui and Irongate were available from June The following step by step description details the process used to produce the correlation: 1 The gauged flow records from June 1989, for the Karamu, Awanui and Irongate sites were collated. 2 Where gauged flows for the Awanui and Irongate were not available, they were substituted with a rated mean daily flow value. 3 A scatter plot (Figure 4) was produced to present the data and a linear regression line was drawn showing its equation and the correlation coefficient. 4 To refine the correlation, unreliable (low confidence) data was excluded from the correlation and the reason for exclusion was noted in the spreadsheet. The following was used to assess the reliability of the data: The Awanui and Irongate sites are both weir sites and therefore controlled sites, enabling a stable and reliable rated flow record to be produced for each site. Elevated flows due to artificial releases in the Awanui catchment combined with time lags can produce inbalances in the concurrent gauged and rated flows. Flows recorded under these conditions will not represent the true relationship between the streams, and will skew the correlation. Confidence was reduced in rated flow values at low flows (<100l/s), and gauged or rated flows during winter months (these were infrequent and very high), due to inaccuracies associated with gauging/rating water courses at these extremes. Comparison of the Mangateretere, Awanui, Irongate and Karamu stage and flow records, allowed gauged Karamu flows that were considered to be influenced by the Mangateretere catchment to be excluded from the correlation. Page 10

19 5 After reviewing the data set, the correlation was finalised with an R² value of A total of 59 data points were used in the correlation. The correlation plot is shown in Figure 4. Figure 4. Correlation: Karamu Stream Flow Vs Awanui + Irongate Stream Flow Karamu (l/s) y = x R 2 = Awanui + Irongate (l/s) 8 A recommendation after external review by Mike Harkness (MWH), was to produce the correlation using only natural flows in order to derive a true natural flow relationship between the sites. A second correlation was produced, whereby all gauged and rated flows were naturalised. This correlation produced the same R 2 value of 0.90 but a slightly different slope and constant (y= x ). A second naturalised flow record was produced using the natural correlation. In the low flow range (<2000l/s) of this record, flows were only ±7l/s from the naturalised flow record analysed in this study. As the low flow range is the area of interest in this study, this small difference in flow would not influence the results of this study, but can be taken into account for future use of the data. Page 11

20 2.2 Validation of the Flow Correlation Using the correlation in Figure 4, the un-naturalised Awanui and Irongate daily mean flow records were added together, this compiled flow was input to the correlation equation to produce an un-naturalised Karamu flow record. Testing the robustness of the correlation was possible by comparing the un-naturalised flow record with the gauged Karamu flow record (within the valid flow range for the correlation: 864l/s l/s). The average difference between gauged and correlated flows was 106l/s. Gaugings identified as being affected by a time lag produced the greatest difference in flows. Under stable flow conditions the correlation can be considered robust with an R 2 value of 0.90, giving an approximate standard error of 10%. When flow conditions in the system are not stable, the effect of time lags produces an inbalance in flows between sites. The flow correlation is good based the available data, although there is scatter at the low end of the regression of approximately ±150l/s either side of line, for 1100l/s in the Karamu Stream. The correlation could be further refined by undertaking work such as: Modelling the effect of time lags on the Awanui and Irongate flows. Adjusting the current gauging programme to increase the number of concurrent gaugings in the Karamu system. This will help to better understand the relationships between the mainstem and its tributaries. Undertaking concurrent gaugings during a summer period with abstraction ceased (as per consent conditions) Including additional stream flow records Page 12

21 2.3 Producing the Naturalised Flow Record The derivation of a naturalised flow dataset for a specific site can be complex. The simplest case being a site with a long term flow record and all upstream abstractions being measured or monitored. The abstraction is simply added back into the flow record to produce the naturalised flow. More complex situations arise when knowledge of the flow or flow record is sparse or nonexistent, and little is known of abstraction volumes. The scenario for the Karamu catchment is not a simple situation, although it is not totally unusual in the New Zealand sense. The following details the process used to naturalise the Awanui and Irongate flow records, which are then correlated to produce the naturalised flow record for the Karamu Stream: 1 Using the HBRC s irrigation ban record, the ban periods for the Awanui and Irongate streams, were identified in each flow record. It is assumed a natural flow record would be recorded during an irrigation ban, as no abstraction should be taking place. 2 The irrigation season is taken as being November to April by the HBRC and for the purpose of this work, flows outside of these irrigation months are considered to be natural and unaffected by abstraction. As such only the flows recorded during November to April whilst not under an irrigation ban need to be naturalised. 3 The irrigation ban record for the Hawke s Bay region starts in No record for irrigation bans could be found prior to this year. For the purpose of date development it is considered that no bans had been in place for the irrigation seasons between The flow records during the irrigation seasons for this period were consequently naturalised. 4 Based on the report done for the HBRC by Opus in September Flow Naturalisation for Six Hawke s Bay Rivers (Lew et al, 1997), water usage surveys on different catchments in both Hawke s Bay and the South Island determined that on average, 45% of the total allocated water was actually used. As the percentage of allocated water usage is unknown for the Karamu catchment, the 45% figure from 1997 was adopted for use in this study. 5 The total allocated volume of water (including medium to high risk groundwater takes) was calculated in cubic metres per week for the HBRC Environmental Management Committee meeting on 11 July 2007 and then converted into litres per second for both the Awanui (6.2l/s) and Irongate (9.5l/s) streams. Then 45% of this allocated water was added back in to each flow record for November to April periods, excluding periods under ban, thus producing naturalised flow records for the Awanui and Irongate streams. 6 The naturalised flow records for the Awanui and Irongate streams were then added together, and this compiled flow was input to the correlation equation to produce the naturalised Karamu flow record. The full period for this naturalised record is from 13-Jun-89 to 21-Feb-07. The HBRC uses the Hilltop Hydro program to analyse hydrological data (stored either in a Hilltop Database or a Hilltop file). To produce summary statistics such as MALF using Hydro, flow records need to be in complete years. As this work is concerned with the low flow range and minimum flows, it was decided that gaps in the flow data could be removed when they occurred during winter months, when flow was in rising periods and when flow was in recession but where the minimum flow would not be missed. This allowed more years to be analysed to produce statistics such as MALF but without missing critical periods of flow during each year (i.e. the annual minimum flow). The only gap not to be removed, was a period of 75 days from 3-Dec-94 to 16-Feb-95. It was very Page 13

22 likely that the lowest flow of the 94/95 summer would have occurred during this period, as prior to this period flows were receding, the Irongate record showed its lowest flow for the summer, and low rainfall was recorded at local rainfall stations (e.g. Awanui Stream at Flume rain gauge). This gap in the Karamu record was due to a gap in the Awanui record. The removal of appropriate gaps produced a total of 15 complete years of naturalised Karamu flow record ( and ). Page 14

23 3.0 NATURALISED KARAMU FLOW STATISTICS A plot of the naturalised flow (Figure 21) record is included in Appendix 2, and tables of daily mean flow values in the form of PDay outputs from the Hilltop Hydro program are included in Appendix 3. Using the Hilltop Hydro program, the flow record was analysed and the following statistics in Table 4 were produced. The naturalised Karamu flow record was stored as a Hilltop file. Table 4. Karamu Flow Statistics Statistic Flow l/s Produced From MALF (l/s) Summer (Nov-Apr) 7 Day Moving Mean (Complete Years) Summer 7 Day Q Summer (Nov-Apr) 7 Day Moving Mean for Full Period Minimum Full Period Maximum Full Period Mean Full Period Current Set Minimum Flow 1100 RRMP Catchment Area 467km 2 Full Period of Record 19 Complete Years of Record 15 Figures 5 and 6 (and Table 6 Appendix 1) show the flow duration curve for the naturalised Karamu flow record. Figure 5. Flow Duration Curve for the Naturalised Karamu Flow Record (7 Day Moving Mean for Nov-Apr) - Full Range Daily Mean Flow l/s Percentile Page 15

24 Figure 6. Flow Duration Curve for the Naturalised Karamu Flow Record (7 Day Moving Mean for Nov-Apr) - Low Flow Range ( l/s) Daily Mean Flow l/s Percentile Page 16

25 4.0 LOWFAT MODELLING The Low Flow Analysis Tool (LowFAT) is a computer program developed by NIWA Science. It has been designed as a tool to help with water resource management (Snelder et al, 2001). LowFAT can be used to analyse/interpret the effect of water allocation and restriction decisions on both in-stream values and water abstractors. To use the program, a naturalised historical flow record, minimum flow and abstraction demand are required (as shown in previous sections). LowFAT can model the effects on an historical flow record, by changing the abstraction demand or minimum flow. The modified historical flow series that LowFAT produces, can represent future conditions under different possible abstraction scenarios based on water resource management decisions. The LowFAT program requires a flow series in the form of a Tideda file, to be loaded into the program for analysis. The naturalised Karamu flow record was converted from a Hilltop Manager file into a Tideda file for this purpose. 4.1 LowFAT Abstraction Scenarios In the case of this study, different abstraction scenarios were set to see the effects of changing the allocatable volume (in effect the abstraction demand) on the naturalised Karamu flow record. The existing minimum flow on the Karamu of 1100l/s set by Policy 74 in the RRMP, was used for all scenarios. Two LowFAT projects were created to model different abstraction demands: LowFAT Project 1 - Scenarios with the abstraction demand based on a maximum constant rate of take, applied to the months of the irrigation season LowFAT Project 2 - Scenarios with the abstraction demand based on a maximum instantaneous rate of take, applied to the entire flow record LowFAT Project 1 The following abstraction scenarios use allocatable volumes (abstraction demand) based on a maximum constant rate of take in litres per second: AV30 - Allocatable volume set at 30l/s, this is the current allocatable weekly volume (18023m 3 /wk) set by Policy 74 of the RRMP, applied as a maximum constant rate of take AV37 - Allocatable volume set at 37l/s, this is the current allocated weekly volume of all consents subject to the renewal process (22445m 3 /wk) including medium-high risk surface water depleting groundwater takes, applied as a maximum constant rate of take AV25 - Allocatable volume set at 25l/s (15120m 3 /wk) AV50 - Allocatable volume set at 50l/s (30240m 3 /wk) AV75 - Allocatable volume set at 75l/s (45360m 3 /wk) AV100 - Allocatable volume set at 100l/s (60480m 3 /wk) AV125 - Allocatable volume set at 125l/s (75600m 3 /wk) Page 17

26 4.1.2 LowFAT Project 2 If all consented takes were to exercise their maximum rate of take at a single point in time, this would be the largest potential impact from abstraction on flow in the Karamu Stream. The following abstraction scenarios apply different instantaneous rates of take (current and possible rates) as the abstraction demand, to identify the potential impacts on stream flow: AV183 - Allocatable volume set at 183l/s, which is the maximum instantaneous rate for all current consented takes (not including surface water depleting groundwater takes) AV150 - Allocatable volume set at 150l/s AV200 - Allocatable volume set at 200l/s AV225 - Allocatable volume set at 225l/s AV250 - Allocatable volume set at 250l/s AV300 - Allocatable volume set at 300l/s Page 18

27 4.2 Scenario Results - LowFAT Project 1 For each abstraction scenario the following was produced: A modified flow series (exported from LowFAT) - this was saved as a Hilltop Manager file to be analysed using Hilltop Hydro Flow duration curves and tables - these were produced from the modified flow series using Hilltop Hydro The number of restriction days 9 that would have occurred - exported from LowFAT and presented in plots and tables Flow Distributions Figure 6 shows a comparison between the flow duration curves (over full period of record) for each LowFAT Project 1 scenario s modified flow series. For each scenario the different abstraction demands have been applied to the months during the irrigation season (November to April). From Figure 7 the difference in flow duration can be seen for each scenario. As the abstraction demand increases the flow is reduced for more of the time. Above the minimum flow of 1100l/s, the different scenarios become quite separated at the 95 th percentile. The naturalised flow record has a 95 th percentile of l/s compared to the highest abstraction demand (AV 125) which is l/s (figures taken from Table 7 in Appendix 1), a difference of approximately 5%. At the lower percentiles (15 th percentile), the flow duration curves are much closer. The abstraction demand is only applied to irrigation season, meaning the higher winter flows are not effected by abstraction. This is why the flow duration at the higher range is very similar for each scenario. Figure 7. Flow Duration Curve Comparison Between each LowFAT Project 1 Scenario and the Naturalised Flow (7 Day Moving Mean for Full Period) Naturalised Flow AV25 AV30 AV37 AV50 AV75 AV100 AV Daily Mean Flow l/s Percentile 9 Restriction days includes days where 1-100% restriction of abstraction would occur Page 19

28 Figure 8. Flow Duration Curve Comparison Between each LowFAT Project 1 Scenario and the Naturalised Flow (7 Day Moving Mean for Full Period) - Low Flow Range ( percentiles) Naturalised Flow AV25 AV30 AV37 AV50 AV75 AV100 AV Daily Mean Flow l/s Percentile Page 20

29 4.2.2 Restriction Days Table 5 and Figure 9 show a comparison between the days of restriction for the different LowFAT scenarios, and the days of restriction in the Irrigation Ban Record. The comparison clearly shows that as the abstraction demand (allocatable volume) is increased for each different scenario, the number of restriction days also increase. Two of the scenarios, 30l/s (current allocatable volume) and 37l/s (current allocated volume), show the number of predicted days under ban, reasonably similar to the Irrigation Ban Record. These scenarios best represent the current situation in the Karamu catchment, but still predict a higher number of days under ban than the actual record. The differences between actual and predicted restriction is due to: LowFAT being a model is not reality, it is only at best an estimate of reality. The more data available the closer a model can simulate reality. LowFAT models the effect of abstraction on historical data, where as the irrigation ban record reflects the restrictions enforced based on decisions and information available at the time. Irrigation bans can only be issued based on gauged flows not rated flows. Therefore there is potential for delay in declaring irrigation restrictions when based on weekly gaugings. Table 5. Restriction Days for each LowFAT Project 1 Scenario and the Irrigation Ban Record Year Total Predicted Days of Restriction for Each LowFAT Project 1 Scenario Irrigation Ban AV25 AV30 AV37 AV50 AV75 AV100 AV125 Record 89/ / / / / / / / / / / / / / / / / / Incomplete Year of Data - No Available Data Page 21

30 Figure 9. Restriction Days for LowFAT Project 1 Scenarios Compared to the Irrigation Ban Record MF1100+AV25 MF1100+AV30 Total Days of Restriction MF1100+AV37 MF1100+AV50 MF1100+AV75 MF1100+AV100 MF1100+AV125 Irrigation Ban Record /96 96/97 97/98 98/99 99/00 00/01 01/02 02/03 03/04 04/05 05/06 Irrigation Season Figure 10 (and Table 10 Appendix 1) shows the average number of restriction days for each month. There is often very little difference between scenarios AV25, AV30, AV37 and AV50. Restriction days are highest on average during January, February and March, for every scenario. This is expected as the lowest flows occur during these months (see Table 9 Appendix 1). The lowest minimum flows for January, February and March in the naturalised flow record, are 994l/s, 961l/s and 949l/s respectively. Figure 10. Average Restriction Days Per Month for each LowFAT Project 1 Scenarios Restriction Days MF1100+AV25 MF1100+AV30 MF1100+AV37 MF1100+AV50 MF1100+AV75 MF1100+AV100 MF1100+AV Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Month Page 22

31 4.3 Scenario Results - LowFAT Project 2 For each abstraction scenario the following was produced: A modified flow series (exported from LowFAT) - this was saved as a Hilltop Manager file to be analysed using Hilltop Hydro 10 plots comparing the naturalised flow record against each modified flow series, for the 10 worst low flow periods in the record Flow duration curves and tables - these were produced from the modified flow series using Hilltop Hydro Tables of daily mean flow values for each abstraction scenario in the form of PDay outputs from the Hilltop Hydro program (Appendix 2) Flow Comparisons for 10 Worst Low Flow Periods The effects of the different instantaneous abstraction rates is best seen by comparing the naturalised flow record with the modified flow records. The impact on streamflow in the Karamu is greatest at low flow periods, and most critical when flows approach the minimum flow (1100l/s). The 10 worst low flow periods in the Karamu flow record were selected for analysis. These 10 periods were selected by producing a plot of the naturalised Karamu flow record as a monthly moving mean (Figure 23 Appendix 1). A selection line was introduced and raised up through the flow range until the 10 low flow periods were intersected by the selection line. Figures 11 to 20 plot each low flow period for the abstraction scenarios. Page 23

32 Figure 11. Low Flow Period 1 January June Naturalised Flow (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Minimum Minimum Flow Flow 1100l/s 1100l/s Jan-90 Feb-90 Mar-90 Apr-90 May-90 Jun-90 Flow (l/s) Figure 12. Low Flow Period 2 December May Naturalised Flow(l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Minimum Minimum Flow Flow 1100l/s 1100l/s 2000 Dec-90 Jan-91 Feb-91 Mar-91 Apr-91 May-91 Flow (l/s) Page 24

33 Figure 13. Low Flow Period 3 December June Naturalised Flow (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Minimum Minimum Flow Flow 1100l/s 1100l/s 2000 Dec-97 Jan-98 Feb-98 Mar-98 Apr-98 May-98 Jun-98 Flow (l/s) Figure 14. Low Flow Period 4 December May Naturalised Flow(l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Minimum Minimum Flow Flow 1100l/s 1100l/s Dec-98 Jan-99 Feb-99 Mar-99 Apr-99 May-99 Flow (l/s) Page 25

34 Figure 15. Low Flow Period 5 December June Naturalised Flow (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Minimum Minimum Flow Flow 1100l/s 1100l/s Flow (l/s) Dec-99 Jan-00 Feb-00 Mar-00 Apr-00 May-00 Jun-00 Figure 16. Low Flow Period 6 December June Dec-00 Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Flow (l/s) Naturalised Naturalised Flow Flow (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Minimum Flow 1100l/s Page 26

35 Figure 17. Low Flow Period 7 November May Naturalised Flow(l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Minimum Flow Flow 1100l/s 1100l/s Flow (l/s) Nov-02 Dec-02 Jan-03 Feb-03 Mar-03 Apr-03 May-03 Figure 18. Low Flow Period 8 November June Naturalised Flow(l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Minimum Minimum Flow Flow 1100l/s 1100l/s 2000 Nov-04 Dec-04 Jan-05 Feb-05 Mar-05 Apr-05 May-05 Jun-05 Flow (l/s) Page 27

36 Figure 19. Low Flow Period 9 December May Naturalised Flow(l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Abstaction AV (l/s) Minimum Minimum Flow Flow 1100l/s 1100l/s Flow (l/s) Dec-05 Jan-06 Feb-06 Mar-06 Apr-06 May-06 Figure 20. Start of Low Flow Period 10 December February Naturalised Flow AV150 AV183 AV200 AV225 AV250 AV300 Minimum Flow 1100l/s Dec-06 Jan-07 Flow (l/s) Feb Page 28

37 4.3.2 Flow Duration Figure 21 (and Table 8 Appendix 1) show the flow duration curve for each abstraction scenario compared to the naturalised flow record. From Figure 21 the effect of abstraction demand can be seen. The naturalised flow record sustains higher flows for a greater percent of the time, than any modified flow series with the abstraction demands applied. Flow is above 1500l/s for 82% (299 days) of the time for the naturalised flow record and 65% (237 days) of the time under the AV300 scenario. For example, the naturalised flow sustains flows of 1300l/s for 90% duration. Abstraction demands reduce flows to between 1160l/s (AV150) and 1105l/s (AV300) for the same 90% duration. Figure 21. Flow Duration Curve Comparison Between each LowFAT Project 2 Scenario and the Naturalised Flow (7 Day Moving Mean for Full Period) Naturalised Flow AV150 AV183 AV200 AV225 AV250 AV Daily Mean Flow l/s Percentile Page 29

38 5.0 RESULTS 5.1 Karamu Flow Statistics The statistics in Table 1 (Section 3) show that the MALF for the Karamu Stream at Floodgates is l/s. This was calculated from 15 years of effective record (complete years of data). The summer 7 Day Q95 (November to April) was calculated as l/s. The method adopted in the RRMP, defines the allocatable volume as being the difference between the summer 7-day Q95 flow and the minimum flow. This is calculated below using the statistics produced from the naturalised Karamu flow record: l/s (Summer 7 Day Q95) 1100l/s (Minimum Flow) = l/s The Summer 7 Day Q95 is lower than the minimum flow indicating that using this method there is no water available for allocation, meaning that the Karamu Stream is already over allocated (current allocatable volume = 18023m 3 /wk or 30l/s). If the minimum flow was calculated using the same methods used in 1998 as part of the SLFP, where 80% of an estimated MALF was adopted as the minimum flow (a MALF rate of 80% was adopted as the minimum flow in small streams to allow for the droughts that occur frequently in the region - Te Karamu, 2006), the following calculation could be used: l/s (MALF) x 80 = l/s 100 The result of l/s indicates a much lower minimum flow could be set. However calculating the minimum flow in this way does not take into account environmental and habitat values for the Karamu Stream. Based on a minimum flow equal to 80% of the MALF, the following allocatable volume for the Karamu Stream would be derived: l/s (Summer 7 Day Q95) l/s (Minimum Flow) = l/s (86390m 3 /wk) This calculated allocatable volume of 86390m 3 /wk is 4.8 times the current allocatable volume of 18023m 3 /wk. 5.2 LowFAT Project 1 Scenarios Modelling the effect of a constant abstraction rate in the Project 1 scenarios, whereby a weekly allocated volume is not exceeded, clearly shows (in Figures 7 and 8) that as the rate of abstraction is increased, flow variability in the low flow range is reduced. There is little effect on the higher flow range as abstraction does not occur during the winter months. With the increase in abstraction rate for the different scenarios, the number of days of restriction predicted by LowFAT also increase. Comparing the days of restriction for each scenario, and the irrigation ban record (Table 5 and Table 11 Appendix 1), shows that increasing abstraction from the Karamu Stream will increase the restriction. When comparing the highest allocation level in scenario AV125 to the current allocated volume represented by scenario AV37, restriction could be increased by up to 38 days (53%). 5.3 LowFAT Project 2 Scenarios In the Project 2 scenarios, abstraction demands which apply a maximum instantaneous rate of take, can at any one point in time reduce flows by a considerable amount depending on the rate Page 30

39 of abstraction. This is most critical in the low flow area, where streamflow could be lowered past the minimum flow (1100l/s) threshold, if not for restrictions being put in place. LowFAT models these scenarios using the historical flow record, and applies abstraction restrictions exactly at the right time to prevent streamflow dropping below the minimum flow threshold (this is evident in Figures 11 to 20). In real time this would not be possible as the control site is monitored/operated though manual gaugings. Page 31

40 6.0 CONCLUSIONS 6.1 Karamu Flow Statistics Calculating a minimum flow from the naturalised Karamu flow record, using the same methods as in 1998 (SLFP) produced a lower minimum flow of l/s. If a lower minimum flow was established, more water would be available for allocation. 6.2 LowFAT Project 1 Scenarios Increasing the abstraction demand lowers streamflow in the Karamu, closer to the current minimum flow (1100l/s) for longer. Restriction days are increased directly as a result of the increased abstraction demand, with periods of restriction extended at the start and end of each period. Increasing allocation from 37l/s (AV37 - current allocated volume) to 125l/s (AV125) could increase restriction by 53%. 6.3 LowFAT Project 2 Scenarios The higher the total instantaneous rate of abstraction from all consented takes, the greater and quicker the impact on streamflow, and the more difficult to manage (through restriction) it becomes. The estimation of the volume of abstraction is a major uncertainty in the process used to produce the naturalised Karamu flow record (Section 2.3). A value of 45% of allocated volume was assumed as the actual abstraction for the Karamu catchment based on the work by Lew et al (1997). This uncertainty in the estimate of the amount of abstraction from the system, in turn creates uncertainty in the naturalised flow results and therefore the volume available for allocation. Page 32

41 7.0 FURTHER INVESTIGATIONS The correlation used to derive a naturalised flow record for the Karamu Stream could be further refined by undertaking work such as: Modelling the effect of time lags on the Awanui and Irongate stream flows. Adjusting the current gauging programme to increase the number of concurrent gaugings in the Karamu system. This will help to better understand the relationships between the mainstem and its tributaries. As further flow data and gaugings are collected, continue to update the correlation and naturalised flow record. Undertaking concurrent gaugings during a summer period with abstraction ceased (as per consent conditions. Including additional stream flow records. Further work which could help in setting allocation volumes on the Karamu Stream include: Undertaking a new water usage survey on the Karamu catchment, identifying current quantities of abstraction and discharge taking place in the catchment. Any differences/changes in water usage identified in a new survey compared to the survey undertaken in 1997 by Opus (Lew et al, 1997), could be highlighted, allowing for any adjustments to be made to the naturalised Karamu flow record. Improved data would allow the current correlation method to be improved and would also aid any modelling Undertaking a study looking at the affects of flow variability on stream velocities. Identifying the relationship between flow and velocity will help understand the affect on habitat value in the Karamu Stream, and how best to protect its in-stream values. An alternative method to derive naturalised flow for the Karamu Stream would be to develop a water balance model. A model could be constructed for the catchment that uses daily rainfall and evaporation to estimate daily runoff. Catchment processes such as storage, attenuation and artificial releases would add challenges to the development of a model, but a satisfactory representation should be able to be made. Undertaking an instream habitat assessment suitable for the Karamu Stream to derive an appropriate minimum flow. Modelling a range of minimum flow values with abstraction scenarios to assess impacts on stream flow and days of irrigation restrictions. Page 33

42 8.0 ACKNOWLEDGEMENTS The author would like to thank the following for their help with this report: Brett Stansfield (HBRC Surface Water Quality Scientist) Darryl Lew (HBRC Manager Environmental Regulation) Kim Coulson (HBRC Environmental Data Analyst) Larry Withey (HBRC Team Leader Environmental Information) Michelle Armer (HBRC Environmental Data Technician) Mike Harkness (MWH New Zealand Ltd Senior Hydrologist) Ross Woods (NIWA Science Hydrologist) Roddy Henderson (NIWA Science Hydrologist) Tom Brooks (HBRC Groundwater Quantity Scientist) Page 34

43 9.0 REFERENCES Hawke s Bay Regional Council 1995, Hawke s Bay Regional Council - Regional Water Resources Plan, Environmental Management Group Technical Report, Operative Hawke s Bay Regional Council 2006, Hawke s Bay Regional Resource Management Plan (RRMP), Environmental Management Group Technical Report, Operative 28 August Hawke s Bay Regional Council 2004, Te Karamu - Catchment Review and Options for Enhancement, Environmental Management Group Technical Report. Larking, R 2004, Groundwater Development in the Ruataniwha Consents Zone, Hawke s Bay Regional Council - Environmental Management Group Technical Report. Lew, D 2007, Issues associated with the processing of Karamu catchment renewals, Agenda Item 7, Hawke s Bay Regional Council - Environmental Management Committee, 11 July Lew, DDF, Waugh, JR, Ong, SW & Gore, LW 1997, Flow Naturalisation for Six Hawke s Bay Rivers - Hawkes Bay Regional Council, Wellington, OPUS International Consultants Limited. Porter, SE & Cairns, IH 1990, Minimum low flows: Karamu Stream Catchment, Agenda Item: Hawke s Bay Regional Council - Planning and Policy Standing Committee, 11 July Snelder, T, Kingsland, S, Walsh, J & Carter, G 2001, Low Flow Analysis Tool (LowFAT 1.03) User Manual, Christchurch: NIWA. Woods, G 2002, Sustainable Low Flow Project: Regional Report. Hawke s Bay Regional Council - Environmental Management Group Technical Report Page 35

44 APPENDIX 1 Page 36

45 Table 6. Flow Duration for the Karamu Naturalised Flow Record (7 Day Moving Mean for Nov-Apr) Page 37

46 Table 7. Flow Duration for each LowFAT Project 1 Scenario and the Naturalised Flow Page 38

47 Table 8. Flow Duration for each LowFAT Project 2 Scenario and the Naturalised Flow Page 39

48 Table 9. Monthly Minimum Flow Summary for the Naturalised Karamu Flow Record Year Monthly Minimum Flow (l/s) Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun Min Mean Max The Min Mean and Max of Annual values are for complete years only Incomplete Data For month Page 40

49 Table 10. Average Number of Restriction Days Per Month for Each Project 1 LowFAT Scenario LowFAT Scenario Average No. of Restriction Days Per Month Jul Aug Sep Oct Nov Dec Jan Feb Mar Apr May Jun MF1100+AV MF1100+AV MF1100+AV MF1100+AV MF1100+AV MF1100+AV MF1100+AV Irrigation Season Table 11. Comparison Between Restriction Days for Increased Abstraction Scenarios and the Irrigation Ban Record Year Restriction Days for LowFAT Project 1 Increased Abstraction Scenarios Irrigation Ban AV50 AV75 AV100 AV125 Record (IBR) Predicted Diff from IBR Predicted Diff from IBR Predicted Diff from IBR Predicted Diff from IBR 94/ / / / / / / / / / / / / Incomplete Year of Data - No Available Data Page 41

50 Figure 22. Karamu Stream at Floodgates Naturalised Daily Mean Flow Record Page 42

51 Figure 23. Worst Low Flow Periods in Karamu Stream at Floodgates Daily Mean Flow Record Page 43

52 APPENDIX 2 Page 44

53 PDay Outputs For Naturalised Karamu Flow Record Page 45

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60 PDay Outputs For LowFAT Project 2 Scenarios Page 52

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